{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 滤波代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 滤波代码\n",
    "import pywt\n",
    "from scipy import signal\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "def Wavelet_transform(ECG_Q):\n",
    "    w = pywt.Wavelet('db8') # 选用Daubechies8小波\n",
    "    maxlev = pywt.dwt_max_level(len(ECG_Q), w.dec_len)\n",
    "    threshold = 0.1 # Threshold for filtering\n",
    "    coeffs = pywt.wavedec(ECG_Q,'db8', level=maxlev) # 将信号进行小波分解\n",
    "    #这里就是对每一层的coffe进行更改\n",
    "    for i in range(1, len(coeffs)):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i])) \n",
    "    datarec = pywt.waverec(coeffs, 'db8') # 将信号进行小波重构\n",
    "    return datarec\n",
    "\n",
    "def butter_fliter(data, frequency, highpass, lowpass):\n",
    "    [b, a] = signal.butter(3, [lowpass / frequency * 2, highpass / frequency * 2], 'bandpass')\n",
    "    Signal_pro = signal.filtfilt(b, a, data)\n",
    "    return Signal_pro\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "' 这里有好多个ECG变量,在此做个说明,\\n    ECG_01是读出硬件给过来的数据集,ECG_02是把ECG_01从一维转化为二维,\\n    ECG_03是把ECG_02塑造成想训练的形状,ECG_04是对ECG_03进行滤波,\\n    ECG_05则是对数组进行标准化,注:其实完全可以在读进文件后就进行滤波操作,这样反而能省事,\\n    不过写都写了,后续有需要的话再做更改吧'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.io as scio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 标准化\n",
    "def standscaler_demo(date):\n",
    "    transfer = StandardScaler()\n",
    "    date1 = transfer.fit_transform(date)\n",
    "    return date1\n",
    "\n",
    "# 数据读进来处理的封装函数\n",
    "def ECG_process(ECG_01):\n",
    "    ECG_02 = np.array(ECG_01).reshape(-1,1)[:102400]\n",
    "    ECG_03 = ECG_02.reshape(-1,512)\n",
    "    ECG_04 = np.zeros(ECG_03.shape)\n",
    "    for line in range(0,ECG_03.shape[0]):\n",
    "        ECG_04[line] = Wavelet_transform(ECG_03[line])\n",
    "    ECG_05 = standscaler_demo(ECG_04)\n",
    "    return ECG_04,ECG_05   # 返回值为两个数组\n",
    "\n",
    "ECG_01 = pd.read_csv(\"../Datasets/training_data.csv\")[\"x\"] # 训练集数据\n",
    "ECG_04,ECG_05 = ECG_process(ECG_01)\n",
    "\n",
    "# 模拟非正常心电\n",
    "ECG_stimulate = np.random.normal(0,1,ECG_05.shape)\n",
    "ECG_stimulate += ECG_05 # 之前是一团,现在是一个心电的形状\n",
    "# eg.\n",
    "plt.figure(figsize = (10, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"ECG_Right\",color=\"g\")\n",
    "plt.plot(ECG_05[0][:512], color='g') \n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"ECG_Stimulate\",color=\"r\")\n",
    "plt.plot(ECG_stimulate[0][:512], color='r')\n",
    "plt.show()\n",
    "\n",
    "''' 这里有好多个ECG变量,在此做个说明,\n",
    "    ECG_01是读出硬件给过来的数据集,ECG_02是把ECG_01从一维转化为二维,\n",
    "    ECG_03是把ECG_02塑造成想训练的形状,ECG_04是对ECG_03进行滤波,\n",
    "    ECG_05则是对数组进行标准化,注:其实完全可以在读进文件后就进行滤波操作,这样反而能省事,\n",
    "    不过写都写了,后续有需要的话再做更改吧'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确标签值列表 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "错误标签值列表 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "目标值向量是：\n",
      " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "\n",
      "训练集ndarry格式的数据为:\n",
      " [[-0.52916271 -0.5360715  -0.54143314 ... -0.62307416 -0.53845312\n",
      "  -0.40582672]\n",
      " [-0.24974456 -0.2282379  -0.25204163 ...  0.89785936  0.82216482\n",
      "   0.8001749 ]\n",
      " [ 0.46470222  0.45086427  0.42934633 ... -0.53668251 -0.52328366\n",
      "  -0.51723243]\n",
      " ...\n",
      " [ 1.16756737 -1.15529695 -0.71175714 ...  0.7401971   0.80171651\n",
      "  -1.27853666]\n",
      " [-2.343562   -0.59628932  1.86263357 ... -1.76875378  0.21287812\n",
      "   0.01256588]\n",
      " [-1.82718763 -0.59014821 -0.44019416 ... -0.179473   -1.7494707\n",
      "  -1.41477003]]\n"
     ]
    }
   ],
   "source": [
    "#  构建数据集\n",
    "true_list = [1] * 200\n",
    "print(\"正确标签值列表\", true_list)\n",
    "error_list = [0] * 200\n",
    "print(\"错误标签值列表\", error_list )\n",
    "for i in error_list:\n",
    "    true_list.append(i)\n",
    "target_list = true_list\n",
    "print(\"目标值向量是：\\n\",target_list)\n",
    "\n",
    "#  合并两个训练集为ndarray类型的数据\n",
    "np_train = np.vstack((ECG_05,ECG_stimulate))\n",
    "print(\"\\n训练集ndarry格式的数据为:\\n\", np_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分割后用来训练的特征值是:\n",
      " [[-0.29734669 -0.29091043 -0.28770134 ... -0.58728078 -0.56403185\n",
      "  -0.55722577]\n",
      " [ 5.33846173  5.79542958  4.1505868  ...  0.74758976  1.72202213\n",
      "  -0.1387543 ]\n",
      " [-1.40640956  0.21128009 -1.1735219  ... -2.10114599 -0.34968934\n",
      "  -1.00062748]\n",
      " ...\n",
      " [-0.36989419 -0.37086601 -0.37320079 ... -0.38404172 -0.39557383\n",
      "  -0.3954859 ]\n",
      " [ 0.52376741 -0.81945178 -1.92223141 ... -0.39885991  1.83972548\n",
      "   0.6621923 ]\n",
      " [-1.01321408 -0.65445074 -2.29305023 ...  0.91746727  0.89353817\n",
      "   0.67621333]]\n",
      "\n",
      "分割后用来测试的特征值是:\n",
      " [[ 0.53124863 -1.42062525 -0.09799208 ... -0.02123934 -0.50557642\n",
      "  -1.14677501]\n",
      " [ 1.02774134  1.40533656  0.4363293  ...  0.47057009  0.62463678\n",
      "  -1.33203744]\n",
      " [ 0.27437609  0.28350361  0.27365903 ... -0.41880307 -0.34430261\n",
      "  -0.36452735]\n",
      " ...\n",
      " [-0.31781846 -0.28260117 -0.32197381 ...  0.35280667  0.31821535\n",
      "   0.29828609]\n",
      " [-0.96810325  2.73056845  0.01780708 ...  0.69733229 -1.32395146\n",
      "  -1.78999538]\n",
      " [-0.55525186  0.61958189  1.56078287 ... -0.97349936 -2.47814414\n",
      "  -1.26601674]]\n",
      "\n",
      "分割后用来训练的目标值是:\n",
      " [1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0]\n",
      "\n",
      "分割后用来测试的目标值是:\n",
      " [0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "#  进行数据分割\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(np_train, target_list, test_size=0.2)\n",
    "\n",
    "print(\"\\n分割后用来训练的特征值是:\\n\", x_train)\n",
    "print(\"\\n分割后用来测试的特征值是:\\n\", x_test)\n",
    "print(\"\\n分割后用来训练的目标值是:\\n\", y_train)\n",
    "print(\"\\n分割后用来测试的目标值是:\\n\", y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "向量机的预测值是：\n",
      " [0 0 1 0 0 0 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0\n",
      " 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 0\n",
      " 1 0 0 1 0 0]\n",
      "实际值是:\n",
      " [0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0]\n",
      "准确率是：\n",
      " 0.9375\n",
      "auc指标是:\n",
      " 0.938398999374609\n"
     ]
    }
   ],
   "source": [
    "# 向量机\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import pickle\n",
    "\n",
    "estimator = SVC(random_state=6)\n",
    "Svc_train = estimator.fit(x_train, y_train)\n",
    "svc_tra = estimator.score(x_test, y_test)\n",
    "\n",
    "# 模型保存\n",
    "with open (\"II. Classification(SVC).pickle\",\"wb\") as f:\n",
    "    pickle.dump(Svc_train,f) #将训练好的模型svc_tra存储在变量f中，且保存到本地\n",
    "\n",
    "# 模型的加载和使用\n",
    "with open(\"II. Classification(SVC).pickle\",\"rb\") as f:\n",
    "    SVM_load = pickle.load(f)\n",
    "\n",
    "y_pre_svc = estimator.predict(x_test)\n",
    "roc_svc_score = roc_auc_score(y_test, y_pre_svc)\n",
    "svc_tre = estimator.score(x_test, y_test)\n",
    "print(\"向量机的预测值是：\\n\", y_pre_svc)\n",
    "print(\"实际值是:\\n\", y_test)\n",
    "print(\"准确率是：\\n\", svc_tre)\n",
    "print(\"auc指标是:\\n\",roc_svc_score) \n",
    "# II. Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# 标准化\n",
    "def standscaler_demo(date):\n",
    "    transfer = StandardScaler()\n",
    "    date1 = transfer.fit_transform(date)\n",
    "    return date1\n",
    "\n",
    "def ECG_process(ECG_01):\n",
    "    ECG_02 = np.array(ECG_01).reshape(-1,512)  #.reshape(-1,1)[0:ECG_01.shape[0] - ECG_01.shape[0]%1024]\n",
    "    ECG_03 = ECG_02.reshape(-1,512)\n",
    "    ECG_04 = np.zeros(ECG_03.shape)\n",
    "    for line in range(0,ECG_03.shape[0]):\n",
    "        ECG_04[line] = Wavelet_transform(ECG_03[line])\n",
    "    ECG_05 = standscaler_demo(ECG_04)\n",
    "    return ECG_04,ECG_05  # 用python的拆包将滤波后的数组与进行标准化的数组分别返回\n",
    "\n",
    "# 模型的加载和使用\n",
    "with open(\"II. Classification(SVC).pickle\",\"rb\") as f:\n",
    "    SVM_load = pickle.load(f)\n",
    "\n",
    "test_ECG = pd.read_csv(\"../Datasets/1111ECG20230409165336.csv\",header=None)\n",
    "ECG_04,ECG_05 = ECG_process(test_ECG)\n",
    "print(SVM_load.predict(ECG_05))\n",
    "\n",
    "\n",
    "predict_array = SVM_load.predict(ECG_05)\n",
    "plt.plot(ECG_04[7])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16,)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 删掉不符合规范的心电行数\n",
    "\n",
    "# len_ECG = ECG_process(test_ECG)[0].shape[1]\n",
    "# signal = ECG_process(test_ECG)[0]\n",
    "# signal\n",
    "# for i in range(0,predict_array.shape[0]):\n",
    "#     if predict_array[i] == 0:\n",
    "#         signal = np.delete(signal,i,axis=0)\n",
    "#         # print(predict_array[i])\n",
    "# # signal\n",
    "# signal_flatten = signal.flatten()\n",
    "# signal_flatten  #代码有缺陷，易出现前后数量不够所要截取的长度\n",
    "\n",
    "# 拉长，有利于R峰的提取\n",
    "signal_flatten = ECG_04.flatten()\n",
    "for i in range(0,predict_array.shape[0]):\n",
    "    if predict_array[i] == 0:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(signal[5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b84ff33110>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行下采样1024个点到720\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from scipy import signal\n",
    "\n",
    "data = pd.read_csv('d:/下载/1111ECG20230409165414.csv',header=None).to_numpy().reshape((-1,1024))\n",
    "def resample_mapping(data):\n",
    "    # 将本地数据等比映射到MIT数据集\n",
    "    data1 = data / 4096 * 1.6 - 0.6\n",
    "    resample_array = np.ones([8,720])\n",
    "    count = 0\n",
    "    for i in data1:\n",
    "        # resample_list.append(scipy.signal.resample(i, 717, t=None, axis=0, window=None))\n",
    "        resample_array[count] =  signal.resample(i, 720, t=None, axis=0, window=None)\n",
    "        count += 1\n",
    "    # data2 = butter_fliter(data1,frequency=360,highpass=40,lowpass=0.1)\n",
    "    # plt.plot(singnal_fliter(resample_array[2]))\n",
    "    # plt.plot(data[2])\n",
    "    # plt.show()\n",
    "    # singnal_fliter\n",
    "    # resample_array_filter = np.array(resample_array.shape)\n",
    "    # for i in range(0,resample_array.shape[0]):\n",
    "    #     resample_array_filter[i] = butter_fliter(resample_array[i],frequency=360,highpass=40,lowpass=0.1)\n",
    "    return resample_array\n",
    "resample_array = resample_mapping(data)\n",
    "# plt.plot(resample_mapping(data)[0])\n",
    "resample_array\n",
    "plt.plot(resample_array[2])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
